Skill UI

This skill guides users through the critical steps of experimental design, helping plan studies before data collection. It covers selecting appropriate methodologies—such as Factorial, Blocked, Crossover, and DOE designs—and generating randomized layouts. It ensures that experiments are structured correctly to isolate effects, enabling reliable causal claims and robust data interpretation.

This skill provides a deterministic, git-only method to verify claims made by AI agents (e.g., 'done', 'shipped', 'fixed'). Instead of trusting the agent's self-report, it audits the commit's diff and git history using the DOS kernel (`dos verify`, `dos commit-audit`) to confirm if the claimed change genuinely occurred and is backed by non-forgeable evidence. It acts as a critical quality gate.

This技能 is used when analyzing complex propositions, debating, or reviewing arguments within the CrossFrame Suite. It helps users deconstruct claims into testable arguments, identifying hidden premises, strongest counterarguments, evidence requirements, and conditions for withdrawal, ensuring conclusions are robust and non-judgmental.

This skill runs comprehensive benchmarks against structural and adversarial corpora. It measures the performance and cost efficiency of a booster model compared to leading LLM baselines (Gemini, Anthropic). Results are persisted in verifiable JSON reports, crucial for auditing claims and verifying booster win rates before publishing.

This advanced workflow is designed to generate comprehensive, fact-checked research reports on any complex topic. It systematically performs fan-out web searches across multiple defined angles, fetches multiple sources, and employs a rigorous adversarial verification system (3-vote) to validate claims. The final output is a highly cited, synthesized report, ideal for academic or critical business research.

Performs a pure, read-only static security scan of a harness's declared MCP surfaces. It analyzes policy, permission, and dependency issues by reading manifest files (e.g., .mcp/servers.json and .harness/claims.json) without executing any underlying tools. Designed for integration into CI/CD pipelines to enforce security policies and ensure compliance.

This tool synthesizes multiple, scattered research findings retrieved from memory into a single, structured, and actionable report. It automatically grades evidence quality (High/Medium/Low), resolves conflicting claims, and provides concrete, recommendation-based insights, making complex data digestible for decision-making.

Standardized expertise for returns authorization, inspection, grading, disposition decisions, refunds, fraud detection, and warranty claims in retail and e-commerce operations. Useful for managing RMAs, recovery economics, and vendor chargebacks.

This skill reviews a single Markdown troubleshooting note to assess its clarity and completeness. It is designed to identify unclear steps, missing prerequisites, or unverifiable claims within a provided note. Use it when a user asks for feedback to improve an existing troubleshooting document. It provides concise, actionable rewrite suggestions without making any external changes or edits.

This advanced tool cross-checks all factual marketing claims—including statistics, awards, rankings, and performance metrics—against provided source evidence. It classifies claims as Verified, Unverified, or Contradicted, ensuring brand content is accurate, defensible, and minimizes reputational risk. It generates detailed discrepancy reports and suggests compliance-ready corrections.

This comprehensive guide provides authors with best practices for preparing supplementary artifacts for AAAI submissions. It outlines how to structure technical appendices, package reproducible code and data ZIPs, maintain strict anonymity (by removing author metadata and paths), and maximize the evidence for reproducibility. The goal is to ensure that external reviewers can easily verify the research claims without specialized or private tools.

This guide outlines best practices for ensuring rigorous reproducibility of complex interaction claims, especially in game theory and multi-agent systems (MAS). It details required evidence for proofs, experimental setups (agents, opponents, seeds, compute), and consistency checks between the paper's claims and the provided artifacts. Essential for high-stakes academic submissions like AAMAS.